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© Research
Publication : 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Nu3D: 3D Nuclei Segmentation from Light-Sheet Microscopy Images of the Embryonic Heart

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) - 13 Apr 2021

Rituparna Sarkar; Daniel Darby; Héloise Foucambert; Sigolène Meilhac; Jean-Christophe Olivo-Marin

Link to DOI – 10.1109/ISBI48211.2021.9433987

In developmental biology, quantification of cellular morphological features is a critical step to get insight into tissue morphogenesis. The efficacy of the quantification tools rely heavily on robust segmentation techniques which can delineate individual cells/nuclei from cluttered environment. Application of popular neural network methods is restrained by the availability of ground truth necessary for 3D nuclei segmentation. Consequently, we propose a convolutional neural network method, combined with graph theoretic approach for 3D nuclei segmentation of mouse embryo cardiomyocytes, imaged by light-sheet microscopy. The designed neural network architecture encapsulates both membrane and nuclei cues for 2D detection. A global association of the 2D nuclei detection is performed by solving a linear optimization with second order constraint to obtain 3D nuclei reconstruction.

https://ieeexplore.ieee.org/document/9433987